The human hand is one of the most complex and compact grippers that has arisen as a product of natural genetic engineering; it is highly versatile, as it handles power and precision tasks. Since proper contact points and force directions are required to ensure versatility and secure a stable grip on an object, there must be a large workspace and controllable tip force directions for the digits. Although they are important, many individuals with neuromuscular diseases experience loss of these features. Thus, we propose a high-degree-of-freedom (DOF) soft robotic glove inspired by the anatomical features of human hands. The mechanism for adjusting the position and force direction of each tip is based on the structure of the extrinsic and intrinsic muscle–tendon units. The large thumb workspace was achieved by assisting opposition/reposition and flexion/extension to enable various grasping postures. A bidirectional actuation control mechanism with a cable-actuated agonist and an elastomer antagonist increased the assisted DOF and maintained compactness. The kinematic and kinetic performances of our device were evaluated by performing tests with eight stroke survivors. The thumb workspace increased by 43%, 207%, and 248% in the distal–proximal, dorsal–palmar, and radial–ulnar directions, respectively. The pinching shear force decreased by 54% and 45% for the nonthumb digits and thumb, respectively. These device-assisted improvements allowed objects to be stably grasped and manipulated in various postures. The novel device can assist individuals with impaired hand function to improve their grasping performance. Clinical Research Information Service (CRIS) Registration Number: KCT0004855.

For stroke survivors, diminished hand functions limit their ability to perform activities of daily living (ADLs). Recently, soft-robotic gloves have assisted stroke survivors in active rehabilitation by facilitating their finger movements based on intentions expressed through biosignals, such as electromyogram and electroencephalogram. In this regard, helping stroke survivors actively train multiple hand postures can improve hand functions required for ADLs. However, detecting intentions regarding multiple hand postures remains challenging, often resulting in low online classification performance. To address this, we propose a hand rehabilitation system comprising a vision-based intention detection framework and 8-degree-of-freedom soft-robotic glove. Our proposed framework, depth enhanced hand posture intention network, analyzes images and depths data observing users' arm behavior and hand-object interactions to predict intentions for multiple hand postures. The 8-degrees-of-freedom soft-robotic glove facilitates flexion and extension of individual fingers to help users perform desired hand postures. To support active rehabilitation, we operate our glove to facilitate user's finger movements when the user exerts effort to generate desired hand postures. We test our system on a real-time pick and place task involving five hand postures most commonly utilized in ADLs. Our vision-based system could predict and facilitate the desired hand postures for five healthy individuals and three stroke survivors with average accuracy of 90.4 ± 3.6% and 80.3 ± 4.6%, respectively, outperforming methods reported in previous studies.

Swinging-generating high impact by maximizing the speed of motion-is essential in sports activities. However, designing prosthetic hands suitable for swinging is still a challenge. Herein, in this article, we propose a swing-dedicated prosthetic hand that adopts the anatomical features of humans for an efficient swing. Our design, inspired by the joint arthrokinematics and tendon routing of fingers, enables a diagonal power “squeeze” grip robust to impact while increasing the reach of the clubhead. The swing speed is further increased by radio/ulnar deviation of the wrist and its nonlinear stiffness change achieved by the passive clutch mechanism. We evaluated our prosthetic hand through golf and found that it sustained high impact with diagonal grip, and clubhead speed increased by 19% at 90 r/min, with the radio/ulnar deviation nonlinearly correlated with arm angle. Our prosthetic hand design will contribute to improving amputees’ quality of life by allowing them to participate in sports activities.

The finger workspace is crucial for performing various grasping tasks. Thus, various soft rehabilitation gloves have been developed to assist individuals with paralyzed hands in activities of daily living (ADLs) or rehabilitation training. However, most soft robotic glove designs are insufficient to assist with various hand postures because most of them use an underactuated mechanism for design simplicity. Therefore, this paper presents a methodology for optimizing the design of a high-degree-of-freedom soft robotic glove while not increasing the design complexity. We defined the required functional workspace of the index finger based on ten frequently used grasping postures in ADLs. The design optimization was achieved by simulating the proposed finger–robot model to obtain a comparable workspace to the functional workspace. In particular, the moment arm length for extension was optimized to facilitate the grasping of large objects (precision disk and power sphere), whereas a torque-amplifying routing design was implemented to aid the grasping of small objects (lateral pinch and thumb–two-finger pinch). The effectiveness of the optimized design was validated through testing with a stroke survivor and comparing the assistive workspace. The observed workspace demonstrated that the optimized glove design could assist with nine out of the ten targeted grasping posture functional workspaces. Furthermore, the assessment of the grasping speed and force highlighted the glove’s usability for various rehabilitation activities. We also present and discuss a generalized methodology to optimize the design parameters of a soft robotic glove that uses an underactuated mechanism to assist the targeted workspace. Overall, the proposed design optimization methodology serves as a tool for developing advanced hand rehabilitation robots, as it offers insight regarding the importance of routing optimization in terms of the workspace.

Functional rehabilitation of the hand is a complex and difficult process involving a large number of degrees of freedom (DOFs). Soft wearable hand-rehabilitation robots have assisted hand movements with a compact structural design, but effective rehabilitation requires an intuitive control scheme that can manage many DOFs and incorporate interaction with an occupational therapist, which has yet to be developed for this type of device. Thus, we present a soft wearable unilateral telerobotic system that enables various grasping tasks and cooperative interaction between the patient and therapist. The presented system consists of a sensor glove that measures the hand postures of the occupational therapist and a soft robotic glove that assists 4-DOF movements of the patient's hand, including adjustments of the interjoint coordination of the finger and 2-DOF movements of the thumb (flexion/extension and opposition/reposition). The soft robotic glove effectuates hand movements based on the measurements from the sensor glove. A telerobotic impedance-control scheme provides intuitive guidance of various hand postures, along with a fingertip-force vector. The feasible workspace and control performance of the system were evaluated on a healthy recruit and a poststroke patient. The presented system allowed the therapist to increase the patient's thumb workspace by 400% in palmar-dorsal direction and to control the fingertip-force direction at −30°∼10° range by enabling control of interjoint coordination of the proximal-interphalangeal and metacarpophalangeal joints. These features facilitate patients to perform various postures for stable object grasping. The presented rehabilitation system is suitable for noncontact telehealthcare that facilitates patient–therapist interactions.

Objective: This paper presents the development and clinical evaluation of a foot stretching robot that simultaneously stretches the plantar fascia and Achilles tendon for the treatment of plantar fasciitis. The therapeutic effectiveness of the robot and feasibility of using metatarsophalangeal joint stiffness as an indicator of recovery were identified through the clinical evaluations. Methods: The robot implements an effective foot stretching protocol through a novel mechanism design that simultaneously stretches the plantar fascia and Achilles tendon using a single motor. Thirty patients with plantar fasciitis and fifteen healthy participants volunteered in the cross-sectional clinical evaluation, and nine patients from the patients group participated in the one-month clinical trial. Four main outcomes (Foot Function Index, Visual Analogue Scale-Foot and Ankle, plantar fascia thickness, and metatarsophalangeal joint stiffness) were used for the clinical evaluations. Results: In the cross-sectional clinical evaluation, the symptomatic feet of patients showed moderate negative correlation between normalized metatarsophalangeal joint stiffness and plantar fascia thickness with statistical significance. In the one-month clinical trial, all the main outcomes showed significant improvement after using the developed robot. Comparing our results with previous studies also indicated a therapeutic superiority of our robot for treating plantar fasciitis. Conclusion: Our foot stretching robot had significant therapeutic effect on plantar fasciitis, and normalized metatarsophalangeal joint stiffness measured by our robot could be used as a monitoring indicator for recovery from plantar fasciitis. Significance: This study contributed to practical issues related to treatment of plantar fasciitis, and our results could be applied to effective treatment of plantar fasciitis and progressive monitoring of recovery.